ShaRP: Shape-Regularized Multidimensional Projections
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.
Description
CCS Concepts: Human-centered computing -> Visualization techniques; Mathematics of computing -> Dimensionality reduction
@inproceedings{10.2312:eurova.20231088,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Angelini, Marco and El-Assady, Mennatallah},
title = {{ShaRP: Shape-Regularized Multidimensional Projections}},
author = {Machado, Alister and Telea, Alexandru and Behrisch, Michael},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {2664-4487},
ISBN = {978-3-03868-222-6},
DOI = {10.2312/eurova.20231088}
}